import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    参数：
        x: (B, H, W, C) - B批次大小,H高度,W宽度,C通道数
        window_size (int): 窗口大小
    返回：
        windows: (num_windows*B, window_size, window_size, C) - 所有窗口展开的张量
    """
    B, H, W, C = x.shape  # 获取输入张量的维度
    # 将图像重塑为窗口块的形式
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    # 重排维度并合并批次维度与窗口数量维度
    # (1,8,8,7,7) -> (64,7,7,96)  64个7*7的窗口，窗口深度为96
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    参数：
        windows: (num_windows*B, window_size, window_size, C) - 窗口张量
        window_size (int): 窗口大小
        H (int): 图像高度
        W (int): 图像宽度
    返回：
        x: (B, H, W, C) - 还原后的完整特征图
    """
    # 计算批次大小
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    # 重塑张量为原始图像尺寸
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    # 重排维度恢复原始顺序
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    """基于窗口的多头自注意力(W-MSA)模块，包含相对位置偏置。
    支持移位和非移位窗口。

    参数：
        dim (int): 输入通道数
        window_size (tuple[int]): 窗口的高度和宽度
        num_heads (int): 注意力头数
        qkv_bias (bool, optional): 如果为True，为query、key、value添加可学习偏置。默认：True
        qk_scale (float | None, optional): 若设置，则覆盖默认的qk scale值head_dim ** -0.5
        attn_drop (float, optional): 注意力权重的dropout率。默认：0.0
        proj_drop (float, optional): 输出的dropout率。默认：0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.dim = dim  # 输入维度
        self.window_size = window_size  # 窗口大小 (Wh, Ww)
        self.num_heads = num_heads  # 注意力头数
        head_dim = dim // num_heads  # 每个头的维度
        self.scale = qk_scale or head_dim ** -0.5  # 缩放因子

        # 定义相对位置偏置的参数表
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))

        # 获取窗口内每个token的相对位置索引
        coords_h = torch.arange(self.window_size[0])  # 生成高度坐标
        coords_w = torch.arange(self.window_size[1])  # 生成宽度坐标
        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='xy'))  # 生成网格坐标
        coords_flatten = torch.flatten(coords, 1)  # 展平坐标
        # 计算相对坐标
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()
        # 将相对坐标转换为非负索引
        relative_coords[:, :, 0] += self.window_size[0] - 1
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)
        self.register_buffer("relative_position_index", relative_position_index)

        # 定义线性变换层
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)  # QKV线性变换
        self.attn_drop = nn.Dropout(attn_drop)  # 注意力dropout
        self.proj = nn.Linear(dim, dim)  # 输出投影
        self.proj_drop = nn.Dropout(proj_drop)  # 输出dropout

        # 初始化相对位置偏置表
        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        参数：
            x: 输入特征，形状为 (num_windows*B, N, C)
            mask: (0/-inf) 掩码，形状为 (num_windows, Wh*Ww, Wh*Ww) 或 None
        """
        B_, N, C = x.shape  # 获取输入维度
        # QKV变换
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        # qkv格式(qkv,B,head,window_size,head_dim)
        q, k, v = qkv[0], qkv[1], qkv[2]  # 分离QKV

        # 计算注意力分数
        q = q * self.scale  # 缩放query
        attn = (q @ k.transpose(-2, -1))  # 计算注意力权重

        # 添加相对位置偏置
        # TODO 这里真不理解
        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
        attn = attn + relative_position_bias.unsqueeze(0)

        # 应用注意力掩码（如果有）
        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)  # 应用dropout

        # 计算输出
        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)  # 输出投影
        x = self.proj_drop(x)  # 应用dropout
        return x


class SwinTransformerBlock(nn.Module):
    """Swin Transformer块。

    参数：
        dim (int): 输入通道数
        input_resolution (tuple[int]): 输入分辨率
        num_heads (int): 注意力头数
        window_size (int): 窗口大小
        shift_size (int): SW-MSA的移位大小
        mlp_ratio (float): MLP隐藏维度与嵌入维度的比率
        qkv_bias (bool, optional): 若为True，为query、key、value添加可学习偏置。默认：True
        qk_scale (float | None, optional): 若设置，则覆盖默认的qk scale值
        drop (float, optional): dropout率。默认：0.0
        attn_drop (float, optional): 注意力dropout率。默认：0.0
        drop_path (float, optional): 随机深度率。默认：0.0
        act_layer (nn.Module, optional): 激活层。默认：nn.GELU
        norm_layer (nn.Module, optional): 归一化层。默认：nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim  # 输入维度
        self.input_resolution = input_resolution  # 输入分辨率
        self.num_heads = num_heads  # 注意力头数
        self.window_size = window_size  # 窗口大小
        self.shift_size = shift_size  # 移位大小
        self.mlp_ratio = mlp_ratio  # MLP比率

        # 如果输入分辨率小于窗口大小，则不进行窗口划分
        if min(self.input_resolution) <= self.window_size:
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size必须在0-window_size范围内"

        # 定义层结构
        self.norm1 = norm_layer(dim)  # 第一个归一化层
        self.attn = WindowAttention(  # 窗口注意力层
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        # 随机深度
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)  # 第二个归一化层
        mlp_hidden_dim = int(dim * mlp_ratio)  # 计算MLP隐藏维度
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        # 如果使用移位窗口注意力（SW-MSA）
        if self.shift_size > 0:
            # 计算SW-MSA的注意力掩码
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 创建掩码张量
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            # 为不同区域赋予不同的掩码值
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            # 将掩码转换为窗口形式
            mask_windows = window_partition(img_mask, self.window_size)
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            # TODO 不理解
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            # 将掩码转换为注意力掩码（0表示允许注意力，-100表示禁止注意力）
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None  # 不使用移位时无需掩码

        self.register_buffer("attn_mask", attn_mask)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "输入特征的大小错误"

        shortcut = x  # 残差连接
        x = self.norm1(x)  # 第一次归一化
        x = x.view(B, H, W, C)

        # 循环移位（用于SW-MSA）
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x

        # 窗口划分
        x_windows = window_partition(shifted_x, self.window_size)  # 将特征图划分为窗口
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)

        # W-MSA/SW-MSA注意力计算
        attn_windows = self.attn(x_windows, mask=self.attn_mask)

        # 合并窗口
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)

        # 反向循环移位
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN前馈网络
        x = shortcut + self.drop_path(x)  # 第一个残差连接
        x = x + self.drop_path(self.mlp(self.norm2(x)))  # FFN和第二个残差连接

        return x

    def extra_repr(self) -> str:
        """额外的表示信息"""
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        """计算FLOPs（浮点运算数）"""
        flops = 0
        H, W = self.input_resolution
        # norm1的计算量
        flops += self.dim * H * W
        # W-MSA/SW-MSA的计算量
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # MLP的计算量
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2的计算量
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    """补丁合并层。

    参数：
        input_resolution (tuple[int]): 输入特征的分辨率
        dim (int): 输入通道数
        norm_layer (nn.Module, optional): 归一化层。默认：nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution  # 输入分辨率
        self.dim = dim  # 输入维度
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)  # 降维线性层
        self.norm = norm_layer(4 * dim)  # 归一化层

    def forward(self, x):
        """
        x: B（批次大小）, H*W（特征图大小）, C（通道数）
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "输入特征大小错误"
        assert H % 2 == 0 and W % 2 == 0, f"特征图尺寸 ({H}*{W}) 必须是偶数"

        x = x.view(B, H, W, C)  # 重塑为4D张量

        # 进行2x2步长为2的补丁划分
        x0 = x[:, 0::2, 0::2, :]  # 左上角像素
        x1 = x[:, 1::2, 0::2, :]  # 右上角像素
        x2 = x[:, 0::2, 1::2, :]  # 左下角像素
        x3 = x[:, 1::2, 1::2, :]  # 右下角像素
        x = torch.cat([x0, x1, x2, x3], -1)  # 在通道维度上连接
        x = x.view(B, -1, 4 * C)  # 重塑为3D张量

        # 归一化和降维
        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        """额外的表示信息"""
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        """计算FLOPs（浮点运算数）"""
        H, W = self.input_resolution
        flops = H * W * self.dim  # 归一化的计算量
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim  # 线性层的计算量
        return flops


class BasicLayer(nn.Module):
    """基础的Swin Transformer层，用于一个阶段。

    参数：
        dim (int): 输入通道数
        input_resolution (tuple[int]): 输入分辨率
        depth (int): 块的数量
        num_heads (int): 注意力头数
        window_size (int): 局部窗口大小
        mlp_ratio (float): MLP隐藏维度与嵌入维度的比率
        qkv_bias (bool, optional): 若为True，为query、key、value添加可学习偏置。默认：True
        qk_scale (float | None, optional): 若设置，则覆盖默认的qk scale值
        drop (float, optional): dropout率。默认：0.0
        attn_drop (float, optional): 注意力dropout率。默认：0.0
        drop_path (float | tuple[float], optional): 随机深度率。默认：0.0
        norm_layer (nn.Module, optional): 归一化层。默认：nn.LayerNorm
        downsample (nn.Module | None, optional): 层末尾的下采样层。默认：None
        use_checkpoint (bool): 是否使用检查点来节省内存。默认：False
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim  # 特征维度
        self.input_resolution = input_resolution  # 输入分辨率
        self.depth = depth  # 层深度
        self.use_checkpoint = use_checkpoint  # 是否使用检查点

        # 构建Transformer块
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,  # 交替使用W-MSA和SW-MSA
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # 补丁合并层（下采样）
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        # 依次通过所有Transformer块
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)  # 使用检查点以节省内存
            else:
                x = blk(x)
        # 如果需要，进行下采样
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        """额外的表示信息"""
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        """计算总的FLOPs"""
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()  # 累加每个块的FLOPs
        if self.downsample is not None:
            flops += self.downsample.flops()  # 加上下采样层的FLOPs
        return flops


class PatchEmbed(nn.Module):
    """图像到补丁嵌入

    参数：
        img_size (int): 图像大小。默认：224
        patch_size (int): 补丁token大小。默认：4
        in_chans (int): 输入图像通道数。默认：3
        embed_dim (int): 线性投影输出通道数。默认：96
        norm_layer (nn.Module, optional): 归一化层。默认：None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)  # 转换为二元组
        patch_size = to_2tuple(patch_size)
        # 计算补丁分辨率
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        # 使用卷积实现补丁嵌入
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # 检查输入图像尺寸
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"输入图像尺寸 ({H}*{W}) 与模型不匹配 ({self.img_size[0]}*{self.img_size[1]})."
        # 补丁嵌入、展平和维度转置
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        # 计算卷积和归一化的FLOPs
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class SwinTransformer(nn.Module):
    """Swin Transformer
        PyTorch实现：'Swin Transformer: 使用移位窗口的层次化视觉Transformer' -
          https://arxiv.org/pdf/2103.14030

    参数：
        img_size (int | tuple(int)): 输入图像大小。默认：224
        patch_size (int | tuple(int)): 补丁大小。默认：4
        in_chans (int): 输入图像通道数。默认：3
        num_classes (int): 分类头的类别数。默认：1000
        embed_dim (int): 补丁嵌入维度。默认：96
        depths (tuple(int)): 每个Swin Transformer层的深度
        num_heads (tuple(int)): 不同层中的注意力头数
        window_size (int): 窗口大小。默认：7
        mlp_ratio (float): MLP隐藏维度与嵌入维度的比率。默认：4
        qkv_bias (bool): 若为True，为query、key、value添加可学习偏置。默认：True
        qk_scale (float): 若设置，则覆盖默认的qk scale值。默认：None
        drop_rate (float): Dropout率。默认：0
        attn_drop_rate (float): 注意力dropout率。默认：0
        drop_path_rate (float): 随机深度率。默认：0.1
        norm_layer (nn.Module): 归一化层。默认：nn.LayerNorm
        ape (bool): 若为True，向补丁嵌入添加绝对位置嵌入。默认：False
        patch_norm (bool): 若为True，在补丁嵌入后添加归一化。默认：True
        use_checkpoint (bool): 是否使用检查点来节省内存。默认：False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)  # 层数
        self.embed_dim = embed_dim
        self.ape = ape  # 是否使用绝对位置嵌入
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # 将图像分割为不重叠的补丁
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # 绝对位置嵌入
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # 随机深度
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # 随机深度衰减规则

        # 构建层
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            # 构建基本层
            layer = BasicLayer(
                dim=int(embed_dim * 2 ** i_layer),
                input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                  patches_resolution[1] // (2 ** i_layer)),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=self.mlp_ratio,
                qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        """初始化模型权重"""
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        """指定不进行权重衰减的参数"""
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        """指定不进行权重衰减的参数关键词"""
        return {'relative_position_bias_table'}

    def forward_features(self, x):
        """特征提取前向传播"""
        x = self.patch_embed(x)  # 补丁嵌入
        if self.ape:
            x = x + self.absolute_pos_embed  # 添加绝对位置嵌入
        x = self.pos_drop(x)

        # 通过所有层
        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)  # 展平特征
        return x

    def forward(self, x):
        """模型前向传播"""
        x = self.forward_features(x)
        x = self.head(x)  # 分类头
        return x

    def flops(self):
        """计算总的FLOPs"""
        flops = 0
        flops += self.patch_embed.flops()  # 补丁嵌入的FLOPs
        for i, layer in enumerate(self.layers):
            flops += layer.flops()  # 每层的FLOPs
        # 最后的归一化和分类头的FLOPs
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops


if __name__ == "__main__":
    SW = SwinTransformer()

    inp = torch.randn([1, 3, 224, 224])

    out = SW(inp)

    print(out.shape)
